AnalyticsOps - DevOps for Data Science

Machine Learning and statistical models should be considered their own pieces of software, where the source code is largely unknown and limited debugging capabilities exist. Despite these restrictions, however, these models are finding themselves as standard elements in the data scientist’s toolkit and with greater emphasis on taking a more dominant role in production systems. But how do we scale and operationalize these models into a production environment after a successful proof of concept was conducted?

In our experience, companies require an infrastructure grounded in continuous, automated, and governed builds and tests that are capable of improving Machine Learning and statistical models. Such an environment allows data scientists to have confidence that predictions formed from newly trained models will not fail in production while meeting critical low-latency or high- throughput SLAs.

To the seasoned IT professional, these development workflows and production constraints are nothing new. Application development leveraging DevOps best-practices, software engineering, and IT operations have long brought these disparate challenges together to collaborate towards a single process of software creation and deployment. Building upon this precedence for collaboration, we present an extension of the traditional DevOps and data science disciplines via the introduction of AnalyticOps, an agile data science path-to- production framework based on the principles of heavy automation. Fully rooted in a strong foundation in open source technologies like Docker, Kubernetes, Jenkins and Kafka we present implementations of real-world use-cases from Banking (Real-time Fraud Detection) and Insurance (Real-time Quote Generation) illustrating the value of the AnalyticOps discipline. The
industrialization of analytics will enable companies to lower costs, speed innovation, and bring new production capabilities to market in a fraction of the time while preventing costly vendor lock-ins.